Knowledge Enhancement

Knowledge enhancement focuses on improving the factual accuracy, reasoning capabilities, and up-to-date information of large language models (LLMs) and other AI systems. Current research emphasizes integrating external knowledge sources, such as knowledge graphs and structured databases, into model architectures through techniques like adapter modules, knowledge distillation, and contrastive learning, often within transformer-based frameworks. These advancements are significant for improving the reliability and applicability of AI in various domains, including education, healthcare, and information retrieval, by mitigating issues like hallucinations and knowledge gaps.